TY - EJOU AU - Bhattacharya, Sweta AU - Maddikunta, Praveen Kumar Reddy AU - Meenakshisundaram, Iyapparaja AU - Gadekallu, Thippa Reddy AU - Sharma, Sparsh AU - Alkahtani, Mohammed AU - Abidi, Mustufa Haider TI - Deep Neural Networks Based Approach for Battery Life Prediction T2 - Computers, Materials \& Continua PY - 2021 VL - 69 IS - 2 SN - 1546-2226 AB - The Internet of Things (IoT) and related applications have witnessed enormous growth since its inception. The diversity of connecting devices and relevant applications have enabled the use of IoT devices in every domain. Although the applicability of these applications are predominant, battery life remains to be a major challenge for IoT devices, wherein unreliability and shortened life would make an IoT application completely useless. In this work, an optimized deep neural networks based model is used to predict the battery life of the IoT systems. The present study uses the Chicago Park Beach dataset collected from the publicly available data repository for the experimentation of the proposed methodology. The dataset is pre-processed using the attribute mean technique eliminating the missing values and then One-Hot encoding technique is implemented to convert it to numerical format. This processed data is normalized using the Standard Scaler technique. Moth Flame Optimization (MFO) Algorithm is then implemented for selecting the optimal features in the dataset. These optimal features are finally fed into the DNN model and the results generated are evaluated against the state-of-the-art models, which justify the superiority of the proposed MFO-DNN model. KW - Battery life prediction; moth flame optimization; one-hot encoding; standard scaler DO - 10.32604/cmc.2021.016229